Dataset obtained from: http://www.andrewjanowczyk.com/use-case-6-invasive-ductal-carcinoma-idc-segmentation/ The original dataset consisted of 162 whole mount slide images of Breast Cancer (BCa) specimens scanned at 40x. From that, 277,524 patches of size 50 x 50 were extracted (198,738 IDC negative and 78,786 IDC positive).
Each patch’s file name is of the format:
u_xX_yY_classC.png — > example 10253_idx5_x1351_y1101_class0.png
Where u is the patient ID (10253_idx5), X is the x-coordinate of where this patch was cropped from, Y is the y-coordinate of where this patch was cropped from, and C indicates the class where 0 is non-IDC and 1 is IDC.
#! pip install opencv-python
from glob import glob #Finds all the pathnames matching a specified pattern
import fnmatch #Test whether the filename string matches the pattern string
import cv2 #Reading images
import numpy as np #Math
import matplotlib.pyplot as plt
%matplotlib inline
import keras
from keras.utils import to_categorical
#Train-test split
from sklearn.model_selection import train_test_split
#Resampling data
from imblearn.under_sampling import RandomUnderSampler
#Building the CNN model
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.models import load_model
#Evaluation metrics
from sklearn import metrics
from sklearn.metrics import classification_report, accuracy_score
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import average_precision_score
#Importing required modules
from zipfile import ZipFile
#Specifying the zip file name
file_name = "IDC_regular_ps50_idx5.zip" #This file is in the current working directory
#Opening the zip file in READ mode
with ZipFile(file_name, 'r') as zip:
#Printing all the contents of the zip file
zip.printdir()
#Extracting all the files
print('Extracting all the files now...')
zip.extractall()
print('Done!')